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[ BOOK GROKKING DEEP REINFORCEMENT LEARNING by MIGUEL MORALES OVERVIEW ]

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Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.

Summary
We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the

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Book ID Asin: 1617295450
Book Title: Grokking Deep Reinforcement Learning
Book Author: Miguel Morales
Book Format and Price:
Book Format Name: Kindle
Book Format Price: $36.99
Book Format Name: Paperback
Book Format Price: $43.66-$49.99
Book Price: $49.99
Book Category: Books, Computers & Technology, Computer Science and unknown
Book Rating: 55 ratings

Grokking Deep Reinforcement Learning by Miguel Morales Book Review

Name: Milad
Rating: 5.0 out of 5 stars
Title: Great book to understand major concepts in RL and DRL
Date: Reviewed in the United States on June 27, 2022
Review: The book covers both tabular RL and Deep RL. The author has tried to explain concepts in a very intuitive way by avoiding going deep into maths. However, all the necessary maths has been discussed completely and intuitively. One great point of this book is that it has explanations of many recent major deep RL algorithms.

Name: Chris
Rating: 5.0 out of 5 stars
Title: The best book to go from concepts to code.
Date: Reviewed in the United States on May 23, 2022
Review: This book is an incredible compliment to Sutton. Sutton is great for theory, but Grokking Deep RL will bridge the gap to writing real code that solves real world problems.

Name: David Robles
Rating: 5.0 out of 5 stars
Title: This should be your first RL book
Date: Reviewed in the United States on September 13, 2021
Review: Sutton's book is the bible of RL. But this book is the best introduction to RL.

- Fantastic explanations of complex RL concepts.
- Perfect balance of mixing math and Python code.
- Answers many of the questions that other books don't, all the gotchas of RL.

What Miguel has done with this book is amazing.

Name: Austin W.
Rating: 4.0 out of 5 stars
Title: Kindle version poorly formatted
Date: Reviewed in the United States on February 4, 2023
Review: So far I think the content of this book is good. However the math and code block sections are incredibly difficult to read in the Kindle version due to poor formatting that is not fixable by changing size/margins/etc.

Name: Eli
Rating: 5.0 out of 5 stars
Title: Best intro book on the topic
Date: Reviewed in the United States on February 14, 2023
Review: This book will take you from the very basics of Markov Decision Processes (which form part of the foundation for RL) to state of the art Deep Learning Methods to approximate more difficult environments. The analogies he gives for the techniques are also gold because it gives you an idea of the math present (which is also explained in great depth) and are great if you teach a class and want to drive a point home to a lost student. The code is also explained line by line and he gives you the code to try the results that he derives on your own and experiment with them. There's no reason not to get this book if you want a strong foundation in RL. I'd suggest it as a starting book over Sutton and Barto (which is a great resource on it's own. But this will give you more code examples and will help lay a better foundation for you to appreciate it more).

Name: AZ
Rating: 5.0 out of 5 stars
Title: Fantastic reinforcement learning book
Date: Reviewed in the United States on June 11, 2022
Review: This was my third attempt at trying to learn reinforcement learning. Previously I had tried:
1) David Silver's lectures on YouTube
2) Coursera course on RL from University of Alberta + Sutton & Barto textbook

Both times I felt I would get a vague understanding but still feel confused and after a point I couldn't follow anymore.

Otoh, Grokking Deep Reinforcement Learning has been outstanding. It reads beautifully and it does exactly what the title says. The author goes to great lengths to explain why each method is necessary and then elaborates over each method using text + diagrams + code. I am currently on chapter 7 and very excited to read the rest! Highly recommended!

Name: Jason
Rating: 5.0 out of 5 stars
Title: Written with care. The code is fantastic
Date: Reviewed in the United States on June 23, 2022
Review: The math is beautifully explained, and the code goes far and beyond what you'd expect from a tech book. The repo has many additional code snippets that are super useful when doing actual research

Name: IHeartReading
Rating: 5.0 out of 5 stars
Title: It will take you from novice to state of the art
Date: Reviewed in the United States on January 13, 2021
Review: I had trouble picturing how the Grokking series would be able to cover such a complex topic as deep reinforcement learning. I came to this book with prior exposure to Reinforcement Learning and a short prior exposure to Deep Reinforcement Learning. So while I can’t quite give the perspective of a complete novice, the book did strike me as being very well organized and explained. What really sets this book apart is the annotation of both math formulas and Python code.

One helpful feature of the book was the “Refresh My Memory” sections. In them, the author summarizes relevant concepts presented in earlier chapters just in time to apply them to the new material of the current chapter. This saves the reader from flipping backward to search for the prior references. I found these sections to be completely sufficient to keep moving forward and absorb the new material. I also like that the author explains not just how the algorithms work, but why they work, and presents plots that compare the performance of different algorithms.

The code and Docker instructions on the author’s github repo were very easy to follow (I already had Docker installed). Since I had previous experience with RL, I have not run all of the code, but I did run the code for the more advanced algorithms in chapters 9, 10, and 12. The code itself is well written and clean. The notebooks in early chapters have plenty of comments to help understand what is happening.

I caught almost no typos, which surprised me for a first edition of a sizable book. The writing style is conversational, which helps make a technical subject much less dry.

In summary, if someone were to ask me to recommend one book to take them from a novice to a capable deep reinforcement learning practitioner, I would recommend this one. I would recommend that a reader actively engage with the provided code. Add doc strings and comments to the code as you read it. Then try to rewrite the algorithms yourself from just the math and explanations, or translate the PyTorch portions to TensorFlow to really test your understanding.